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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 277494287 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation
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biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM data and spatial-omics data collected from state-of-the-art
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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
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will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models, and to develop user-friendly tools that will be used by a broad community
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 11 hours ago
Pythonis; and experience with tokamak physics or machine learning techniques. The appointment will be for two years with the possibility of renewal based upon satisfactory job performance. Application
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)-statistics, (applied) mathematics, or a related STEM field. Prior working experience with EHR data, machine learning, NLP, bioinformatics, and large language models (LLM) is preferred. In particular
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clinical features using machine learning and foundational modeling approaches. This work supports disease modeling across chronic kidney disease, acute kidney injury, cancer, and neurological conditions. A
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highly interdisciplinary, integrating big data analysis, state-of-the-art machine learning models, mathematical modeling, and systems biology to elucidate the mechanisms of drug interactions in complex
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, Mathematics, Physics, or a closely related field. Proficiency in machine learning libraries (e.g, scikit-learn, PyTorch, and transformers) and data analysis tools (e.g., pandas, NumPy, and CuPy). Hands